• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

• Quality Improvement in Health Care
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• v.22 no.2
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• pp.43-73
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• 2016

Purpose: The aims of this study were to assess the presence of core patient safety practices in Korean hospitals and assess the differences in reporting and learning systems of patient safety, infrastructure, and safe practices by hospital characteristics. Methods: The authors developed a questionnaire including 39 items of patient safety staffing, health information system, reporting system, and event-specific prevention practices. The survey was conducted online or e-mail with 407 tertiary, general and specialty hospitals. Results: About 90% of hospitals answered the self-reporting system of patient safety related events is established. More than 90% of hospitals applied incidence monitoring or root cause analysis on healthcare-associated infection, in-facility pressure ulcers and falls, but only 60% did on surgery/procedure related events. More than 50% of the hospitals did not adopted present on admission (POA) indicators. One hundred (80.0%) hospitals had a department of patient safety and/or quality and only 52.8% of hospitals had a patient safety officer (PSO). While 82.4% of hospitals used electronic medical records (EMRs), only 53% of these hospitals adopted clinical decision support function. Infrastructure for patient safety except EMRs was well established in training, high-level and large hospitals. Most hospitals implemented prevention practices of adverse drug events, in-facility pressure ulcers and falls (94.4-100.0%). But prevention practices of surgery/procedure related events had relatively low adoption rate (59.2-92.8%). Majority of prevention practices for patient safety events were also implemented with a relatively modest increase in resources allocated. Conclusion: The hospital-based reporting and learning system, EMRs, and core evidence-based prevention practices were implemented well in high-level and large hospitals. But POA indicator and PSO were not adopted in more than half of surveyed hospitals and implementation of prevention practices for specific event had low. To support and monitor progress in hospital's patient safety effort, national-level safety practices set is needed.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.661-668
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• 2018

Biomedical signals using skin resistance have different characteristics according to stress diseases. Biological diagnostic devices for diagnosing stress diseases have been developed by using these characteristics, and devices have been developed so that the signals measured by the skin storage meter can be easily analyzed. Experts in the field will look directly at the output signal to determine the likelihood of any stress disorder. However, it is very difficult for a person to accurately determine whether a person to be measured has a stress disorder by analyzing a bio-signal measured by each person to be measured, and the result of the judgment is very likely to be wrong. In order to solve these problems, we implemented the function of determining the signal of a stress disorder by using the machine learning technique. SVM was used as a classification method in consideration of low computing ability of measurement equipment. Training data and test data were randomly generated for each disease using error range 5 based on 13 diseases. Simulation results showed more than 90% decision accuracy. In the future, if the measurement equipment is actually applied to the patients, we can retrain the classifier with the newly generated data.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.407-410
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• 2006

방대한 양의 다양한 데이터들이 발생되는 의료분야에서는 임상데이터를 기반으로 보다 정확하고 효율적으로 현상을 분석/판단하여 의사가 환자진료 시 정확한 진단과 치료를 수행할 수 있도록 보조해주는 적절한 의사결정지원시스템이 요구되고 있다. 따라서, 이와 같은 요구를 충족시키기 위해서는 다종 다양한 데이터로부터 간결하면서도 효과적으로 개념들을 추출하고 구조화하여 개념계층구조로 표현할 수 있어야 하며, 실세계의 데이터에 대한 구조화와 요약을 제공하고 필요한 정보를 수월하게 접근할 수 있어야 한다. 본 연구에서는, 도메인 내의 다양한 데이터들로부터 개념들을 추출하고, 개념들 사이의 상하위 관계를 파악하여 개념계층구조를 구축하기위한 정형화된 데이터분석기법으로서 형식개념분석기법(Formal Concept Analysis)을 소개하고, 이를 치과 교정학 분야의 환자 임상데이터 분석기법(Cephalometric Analysis)에 융합한 형태의 임상의사결정지원시스템 개발 및 향후 연구과제 등에 관해 설명한다.

• Health Policy and Management
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• v.28 no.3
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• pp.263-271
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• 2018

Health technology assessment (HTA) is defined as multidisciplinary policy analysis to look into the medical, economic, social, and ethical implications of the development, distribution, and use of health technology. Following the recent changes in the social environment, there are increasing needs to improve Korea's healthcare environment by, inter alia, assessing health technologies in an organized, timely manner in accordance with the government's strategies to ensure that citizens' medical expenses are kept at a stable level. Dedicated to HTA and research, the National Evidence-based Healthcare Collaborating Agency (NECA) analyzes and provides grounds on the clinical safety, efficacy, and economic feasibility of health technologies. HTA offers the most suitable grounds for decision making not only by healthcare professionals but also by policy makers and citizens as seen in a case in 2009 where research revealed that glucosamine lacked preventive and treatment effects for osteoarthritis and glucosamine was subsequently excluded from the National Health Insurance's benefit list to stop the insurance scheme from suffering financial losses and citizens from paying unnecessary medical expenses. For the development of HTA in Korea, the NECA will continue exerting itself to accomplish its mission of providing policy support by health technology reassessment, promoting the establishment and use of big data and HTA platforms for public interest, and developing a new value-based HTA system.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.17-33
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• 2005

This paper presents the proposed a classifier of liver cirrhotic step using MR(magnetic resonance) imaging and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were analysis in the number of data was 231. We extracted liver region and nodule region from T1-weight MR liver image. Then objective interpretation classifier of liver cirrhotic steps. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier learned through error back-propagation algorithm. A classifying result shows that recognition rate of normal is $100\%$, 1type is $82.8\%$, 2type is $87.1\%$, 3type is $84.2\%$. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Research in Community and Public Health Nursing
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• v.29 no.2
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• pp.170-183
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• 2018

Purpose: Although innovative interventions using technologies have been introduced in long-term care settings, available evidence is still anecdotal. The purpose of this study is to investigate and synthesize the outcomes of interventions using technologies delivered to nursing home residents. Methods: Published clinical trials were identified through PubMed, CINHAL, Cochrane and PsycINFO databases and manually hand-searching. Eligible studies were articles published between 1997-2016 in English or Korean with a randomized controlled trial or quasi-experimental design in which interventions using technologies were delivered to nursing home residents. Results: A total of 20 studies were selected for this review. Types of interventions using technologies were classified into the electronic documentation technology (n=1), the clinical decision support system (n=1), the safety technology (n=1), the health and wellness technology (n=10), and the social connectedness technology (n=7). Overall resident outcomes indicated that interventions using technologies improved behavioral symptoms and psycho-social outcomes, but mixed results were shown in the aspects of physical function, cognitive function, social relationship and quality of service. Conclusion: This review demonstrates that incorporating technologies into nursing home care have positive effects on residents' psycho-social outcomes and behavioral symptoms. To disseminate the effectiveness of interventions using technologies, further research is needed to determine what mechanisms underlying such relationships exist.

• CELLMED
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• v.6 no.3
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• pp.15.1-15.4
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• 2016

Evidence based practice (EBP) is a system of applying the most current and valid high quality evidence to support clinical decision making in a healthcare setting. In the twenty five years since its inception, EBP has become the accepted benchmark for excellence in healthcare. Although the system emerged within the biomedical sciences, in the years since EBP has become normative across all healthcare modalities from dentistry, allied health to complementary and alternative medicine (CAM). Practicing evidence based medicine within any modality potentially offers the patient the best available care based on high quality evidence. Yet it is the nature of the evidence that provokes some questions about the suitability of EBP across all modalities of healthcare. The meta analysis of randomized controlled trial (RCT) stands at the pinnacle of the hierarchy of evidence in EBP. This forms a challenge to CAM due to the difficulty in reducing the elementals of a holistic naturopathic assessment of a patient into an answerable question to be tested within a RCT. On one level this makes EBP paradigmatically incompatible with CAM, yet on another level it presents the opportunity to redefine the parameters of what is considered high level evidence. EBP has become a tool, and at times a weapon wielded by governments and health insurance companies to direct healthcare funding and policy. The implications of the nature of accepted evidence are becoming far reaching. The pursuit of the best available healthcare for each individual is the focus of EBP. However, the injudicious use of this system to direct health policy is fraught with biomedical bias and dominance. This issue raises the challenge to CAM to present high level evidence according to the rules of evidence, or face the annihilation of centuries of empirical knowledge.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.69-76
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• 2022

This paper proposes a method of refining and processing time-series data using Medical Information Mart for Intensive Care (MIMIC-IV) v2.0 data. In addition, the significance of the processing method was validated through a machine learning-based pressure ulcer early warning system using a dataset processed based on the proposed method. The implemented system alerts medical staff in advance 12 and 24 hours before a lesion occurs. In conjunction with the Electronic Medical Record (EMR) system, it informs the medical staff of the risk of a patient's pressure ulcer development in real-time to support a clinical decision, and further, it enables the efficient allocation of medical resources. Among several machine learning models, the GRU model showed the best performance with AUROC of 0.831 for 12 hours and 0.822 for 24 hours.